Francesca Grisoni

Computational Theory and Mathematics · 2018–2023

A researcher in the Science corpus: 8 papers; trusts 191 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 221 citations into it; #4,654 of 12,182 by trust here, #1,133 by citations
• Materials Chemistry — 175 citations into it; #3,589 of 11,513 by trust here, #7,558 by citations
• Molecular Biology — 160 citations into it; #13,557 of 17,773 by trust here, #9,030 by citations
• Artificial Intelligence — 142 citations into it; #22,476 of 35,345 by trust here, #21,316 by citations
• Computational Chemistry & Drug Discovery — 97 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Safety, Robustness & Interpretability — 40 citations into it
• Model Architectures — 39 citations into it

Whom Francesca Grisoni trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Yann LeCun
3. Diederik P. Kingma
4. Yoshua Bengio
5. J. Urgen Schmidhuber
6. Sepp Hochreiter
7. Alex Graves
8. Alex Krizhevsky
9. Ilya Sutskever
10. Oriol Vinyals

Who trusts Francesca Grisoni overall

1. Kenneth Atz
2. José Jiménez-Luna
3. Gisbert Schneider
4. Shuo Zhang
5. Michelle M. Li
6. Clemens Isert

Papers from Francesca Grisoni's perspective

Ordered by how strongly the researchers Francesca Grisoni trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Drug discovery with explainable artificial intelligence (2020) — 857 citations
• Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators (2018) — 117 citations
• De Novo Design of Bioactive Small Molecules by Artificial Intelligence (2018) — 329 citations
• Artificial intelligence in drug discovery: recent advances and future perspectives (2021) — 319 citations
• Combining generative artificial intelligence and on-chip synthesis for de novo drug design (2020) — 144 citations
• Leveraging molecular structure and bioactivity with chemical language models for de novo drug design (2023) — 142 citations
• Geometric deep learning on molecular representations (2021) — 410 citations
• Bidirectional Molecule Generation with Recurrent Neural Networks (2020) — 203 citations